Lessons-learned of using Monte Carlo method with Important Sampling In Fault Tree Quantification

In the quantitative evaluation of fault trees (FTs) and event trees (ETs) during a level 1 PSA, the combinatoric process of ETs and FTs are necessary to deal with Boolean logic. Most of quantification processes use minimal cut sets, which needs additional process of gaining the minimal cut sets and its validation. Therefore, the need of developing methods that is free of minimal cut sets may have benefits from these viewpoints. While there were some attempts to gain the top vent probability of FTs by using the Monte Carlo method, which could be free from using minimal cut sets by setting a different algorithm, the time and computational cost for using the Monte Carlo method is always the main issue. In order to reduce computational resource and having its strong point in variance reduction, the most frequently used application for the Monte Carlo method is importance sampling. This paper suggests an algorithm of implying importance sampling, a general method used to reduce the cost for the Monte Carlo method, in order to quantify FTs, and show both the application and limitations of importance sampling. An example FT is given in the paper to show the application and algorithm of Monte Carlo method and to imply importance sampling for the quantification process.